Search Engine Domain Transfer

ABSTRACT

A mechanism is provided for search engine domain transfer. The mechanism receives an input query to search a specialized domain from a user and performs a general-domain search based on the input query to generate a set of general-domain results. The mechanism generates a feature vector based on the general-domain results and generates a score for each document within the specialized domain based on the feature vector. The mechanism generates a ranked result set of documents based on the scores of the documents in the specialized domain and presents the ranked result set to the user.

BACKGROUND

The present application relates generally to an improved data processingapparatus and method and more specifically to mechanisms for searchengine domain transfer.

Web search engines work by storing information about many web pages,which they retrieve from the hypertext markup language (HTML) markup ofthe pages. A Web crawler retrieves these pages and follows every link onthe site. The search engine then analyzes the contents of each page todetermine how it should be indexed (for example, words can be extractedfrom the titles, page content, headings, or special fields called metatags). A Web search engine stores data about web pages in an indexdatabase for use in later queries. A query from a user can be a singleword. The index helps find information relating to the query as quicklyas possible. Some search engines store all or part of the source page(referred to as a cache) as as information about the web pages, whereasothers store every word of every page they find. This cached page alwaysholds the actual search text since it is the one that was actuallyindexed, so it can be very useful when the content of the current pagehas been updated and the search terms are no longer in it.

When a user enters a query into a search engine (typically by usingkeywords), the engine examines its index and provides a listing ofbest-matching web pages according to its criteria, usually with a shortsummary containing the document's title and sometimes parts of the text.The index is built from the information stored with the data and themethod by which the information is indexed. Most search engines supportthe use of the Boolean operators AND, OR and NOT to further specify thesearch query. Boolean operators are for literal searches that allow theuser to refine and extend the terms of the search. The engine looks forthe words or phrases exactly as entered. Some search engines provide anadvanced feature called proximity search, which allows users to definethe distance between keywords.

The usefulness of a search engine depends on the relevance of the resultset it gives back. While there may be millions of web pages that includea particular word or phrase, some pages may be more relevant, popular,or authoritative than others. Most search engines employ methods to rankthe results to provide the “best” results first. How a search enginedecides which pages are the best matches, and what order the resultsshould be shown in, varies widely from one engine to another. Themethods also change over time as Internet usage changes and newtechniques evolve. There are two main types of search engine that haveevolved: one is a system of predefined and hierarchically orderedkeywords that humans have programmed extensively. The other is a systemthat generates an “inverted index” by analyzing texts it locates. Thisform relies much more heavily on the computer itself to do the bulk ofthe work.

SUMMARY

In one illustrative embodiment, a method, in a data processing system,is provided for search engine domain transfer. The method comprisesreceiving an input query to search a specialized domain from a user andperforming at least one general-domain search based on the input queryto generate a set of general-domain results. The method furthercomprises generating a feature vector based on the general-domainresults and generating a score for each document within the specializeddomain based on the feature vector. The method further comprisesgenerating a ranked result set of documents based on the scores of thedocuments in the specialized domain and presenting the ranked result setto the user.

In other illustrative embodiments, a computer program product comprisinga computer useable or readable medium having a computer readable programis provided. The computer readable program, when executed on a computingdevice, causes the computing device to perform various ones of, andcombinations of, the operations outlined above with regard to the methodillustrative embodiment.

In yet another illustrative embodiment, a system/apparatus is provided.The system/apparatus may comprise one or more processors and a memorycoupled to the one or more processors. The memory may compriseinstructions which, when executed by the one or more processors, causethe one or more processors to perform various ones of, and combinationsof the operations outlined above with regard to the method illustrativeembodiment.

These and other features and advantages of the present invention will bedescribed in or will become apparent to those of ordinary skill in theart in view of, the following detailed description of the exampleembodiments of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention, as well as a preferred mode of use and further objectivesand advantages thereof, will best be understood by reference to thefollowing detailed description of illustrative embodiments when read inconjunction with the accompanying drawings, wherein:

FIG. 1 is an example diagram of a distributed data processing system inwhich aspects of the illustrative embodiments may be implemented;

FIG. 2 is an example block diagram of a computing device in whichaspects of the illustrative embodiments may be implemented;

FIGS. 3A and 3B illustrate search engine domain transfer in accordancewith an illustrative embodiment;

FIG. 4 is a block diagram illustrating a search engine with domaintransfer in accordance with an illustrative embodiment;

FIG. 5 is a block diagram illustrating query transformation for searchengine domain transfer in accordance with an illustrative embodiment;and

FIG. 6 is a flowchart illustrating operation of a mechanism for searchengine domain transfer in accordance with an illustrative embodiment.

DETAILED DESCRIPTION

A search engine takes a text query from a user and retrieves a list ofdocuments from a corpus, ranked by their relevance to the query. Thebroad domain of information retrieval seeks to optimize the method ofproducing such a ranked list. Tremendous advances have been made in thisfield over the past decades, particularly by Internet search engines,such as Google, Bing, and Baidu. By leveraging a tremendous amount ofdata and highly optimized algorithms, these search engines are todaycapable of interpreting the “intent” users have behind their queries,which are frequently written in natural language.

Current performance of Web search engines is due to two sources of data:a massive corpus of hyperlinked documents (the Internet) and logs ofuser behavior at large scale and fine resolution. While the first sourceof data proves extremely valuable to traditional information retrievalalgorithms (e.g., PageRank), the user query logs are responsible formuch of the recent improvements in the relevancy of Web search results.

The illustrative embodiments provide mechanisms for search engine domaintransfer that focus not on Web search but rather on vertical search,i.e., enterprise search or domain-specific search. Here, the searchengine is designed for a specific domain or application (e.g., searchingthrough a user's email or through a company's website). These customsearch engines have generally struggled to match the performance ofgeneral Web search. This is due to a relative paucity of data: both thedocument corpora and user behavior logs are generally orders ofmagnitude smaller in domain applications.

The mechanisms of the illustrative embodiments allow one to leverage ahighly-optimized wide-domain search engine in order to retrieveinformation from a smaller domain.

There has been a healthy amount of work in the area of vertical search,but this work fails to use broad-domain knowledge or data to powerdomain-specific search. Part of the reason is that domain-specificityencourages highly optimized domain-specific rules.

Google offers a custom search service for websites allowing users tosearch the website's domain. There is an option to enable Google CustomSearch to retrieve both Internet documents and site-specific documents.There is no documented or observable effect where the wide-domain searchfeatures somehow improve the quality of the site-specific results, withthe exception of the autocomplete functionality. This autocompletefunctionality can autocomplete custom search queries based ongeneral-domain user behavior but does not affect functionality beyondquery formation. Furthermore, autocomplete functionality is onlyavailable when a user has selected to search both a custom domain andthe Internet at the same time, and it is unclear if it is even beingleveraged with the intention of improving custom search performance.

In accordance with the illustrative embodiments, a search enginefeaturizes search engine results from a wide-domain search in anefficient manner and applies a resulting feature vector to the task ofquery retrieval within a more specific domain.

Before beginning the discussion of the various aspects of theillustrative embodiments it should first be appreciated that throughoutthis description the term “mechanism” will be used to refer to elementsof the present invention that perform various operations, functions, andthe like. A “mechanism,” as the term is used herein, may be animplementation of the functions or aspects of the illustrativeembodiments in the form of an apparatus, a procedure, or a computerprogram product. In the case of a procedure, the procedure isimplemented by one or more devices, apparatus, computers, dataprocessing systems, or the like. In the case of a computer programproduct, the logic represented by computer code or instructions embodiedin or on the computer program product is executed by one or morehardware devices in order to implement the functionality or perform theoperations associated with the specific “mechanism.” Thus, themechanisms described herein may be implemented as specialized hardware,software executing on general purpose hardware, software instructionsstored on a medium such that the instructions are readily executable byspecialized or general purpose hardware, a procedure or method forexecuting the functions, or a combination of any of the above.

The present description and claims may make use of the terms “a,” “atleast one of,” and “one or more of” with regard to particular featuresand elements of the illustrative embodiments, it should be appreciatedthat these terms and phrases are intended to state that there is atleast one of the particular feature or element present in the particularillustrative embodiment, but that more than one can also be present.That is, these terms/phrases are not intended to limit the descriptionor claims to a single feature/element being present or require that aplurality of such features/elements be present. To the contrary, theseterms/phrases only require at least a single feature/element with thepossibility of a plurality of such features/elements being within thescope of the description and claims.

In addition, it should be appreciated that the following descriptionuses a plurality of various examples for various elements of theillustrative embodiments to further illustrate example implementationsof the illustrative embodiments and to aid in the understanding of themechanisms of the illustrative embodiments. These examples intended tobe non-limiting and are not exhaustive of the various possibilities forimplementing the mechanisms of the illustrative embodiments. It will beapparent to those of ordinary skill in the art in view of the presentdescription that there are many other alternative implementations forthese various elements that may be utilized in addition to, or inreplacement of the examples provided herein without departing from thespirit and scope of the present invention.

The illustrative embodiments may be utilized in many different types ofdata processing environments. In order to provide a context for thedescription of the specific elements and functionality of theillustrative embodiments, FIGS. 1 and 2 are provided hereafter asexample environments in which aspects of the illustrative embodimentsmay be implemented. It should be appreciated that FIGS. 1 and 2 are onlyexamples and are not intended to assert or imply any limitation withregard to the environments in which aspects or embodiments of thepresent invention may be implemented. Many modifications to the depictedenvironments may be made without departing from the spirit and scope ofthe present invention.

FIG. 1 depicts a pictorial representation of an example distributed dataprocessing system in which aspects of the illustrative embodiments maybe implemented. Distributed data processing system 100 may include anetwork of computers in which aspects of the illustrative embodimentsmay be implemented. The distributed data processing system 100 containsat least one network 102, which is the medium used to providecommunication links between various devices and computers connectedtogether within distributed data processing system 100. The network 102may include connections, such as wire, wireless communication links, orfiber optic cables.

In the depicted example, server 104 and server 106 are connected tonetwork 102 along with storage unit 108. In addition, clients 110, 112,and 114 are also connected to network 102. These clients 110, 112, and114 may be, for example, personal computers, network computers, or thelike. In the depicted example, server 104 provides data, such as bootfiles, operating system images, and applications to the clients 110,112, and 114. Clients 110, 112, and 114 are clients to server 104 in thedepicted example. Distributed data processing system 100 may includeadditional servers, clients, and other devices not shown.

In the depicted example, distributed data processing system 100 is theInternet with network 102 representing a worldwide collection ofnetworks and gateways that use the Transmission ControlProtocol/Internet Protocol (TCP/IP) suite of protocols to communicatewith one another. At the heart of the Internet is a backbone ofhigh-speed data communication lines between major nodes or hostcomputers, consisting of thousands of commercial, governmental,educational and other computer systems that route data and messages. Ofcourse, the distributed data processing system 100 may also beimplemented to include a number of different types of networks, such asfor example, an intranet, a local area network (LAN), a wide areanetwork (WAN), or the like. As stated above, FIG. 1 is intended as anexample, not as an architectural limitation for different embodiments ofthe present invention, and therefore, the particular elements shown inFIG. 1 should not be considered limiting with regard to the environmentsin which the illustrative embodiments of the present invention may beimplemented.

As shown in FIG. 1, one or more of the computing devices, e.g., server104, may be specifically configured to implement a search engine withdomain transfer. The configuring of the computing device may comprisethe providing of application specific hardware, firmware, or the like tofacilitate the performance of the operations and generation of theoutputs described herein with regard to the illustrative embodiments.The configuring of the computing device may also, or alternatively,comprise the providing of software applications stored in one or morestorage devices and loaded into memory of a computing device, such asserver 104, for causing one or more hardware processors of the computingdevice to execute the software applications that configure theprocessors to perform the operations and generate the outputs describedherein with regard to the illustrative embodiments. Moreover, anycombination of application specific hardware, firmware, softwareapplications executed on hardware, or the like, may be used withoutdeparting from the spirit and scope of the illustrative embodiments.

It should be appreciated that once the computing device is configured inone of these ways, the computing device becomes a specialized computingdevice specifically configured to implement the mechanisms of theillustrative embodiments and is nota general purpose computing device.Moreover, as described hereafter, the implementation of the mechanismsof the illustrative embodiments improves the functionality of thecomputing device and provides a useful and concrete result thatfacilitates search engine domain transfer.

FIG. 2 is a block diagram of an example data processing system in whichaspects of the illustrative embodiments may be implemented. Dataprocessing system 200 is an example of a computer, such as client 110 inFIG. 1, in which computer usable code or instructions implementing theprocesses for illustrative embodiments of the present invention may belocated.

In the depicted example, data processing system 200 employs a hubarchitecture including north bridge and memory controller hub (NB/MCH)202 and south bridge and input/output (I/O) controller hub (SB/ICH) 204.Processing unit 206, main memory 208, and graphics processor 210 areconnected to NB/MCH 202. Graphics processor 210 may be connected toNB/MCH 202 through an accelerated graphics port (AGP).

In the depicted example, local area network (LAN) adapter 212 connectsto SB/ICH 204. Audio adapter 216, keyboard and mouse adapter 220, modem222, read only memory (ROM) 224, hard disk drive (HDD) 226, CD-ROM drive230, universal serial bus (USB) ports and other communication ports 232,and PCI/PCIe devices 234 connect to SB/ICH 204 through bus 238 and bus240. PCI/PCIe devices may include, for example, Ethernet adapters,add-in cards, and PC cards for notebook computers. PCI uses a card buscontroller, while PCIe does not. ROM 224 may be, for example, a flashbasic input/output system (BIOS).

HDD 226 and CD-ROM drive 230 connect to SB/ICH 204 through bus 240. HDD226 and CD-ROM drive 230 may use, for example, an integrated driveelectronics (IDE) or serial advanced technology attachment (SATA)interface. Super I/O (SIO) device 236 may be connected to SB/ICH 204.

An operating system runs on processing unit 206. The operating systemcoordinates and provides control of various components within the dataprocessing system 200 in FIG. 2. As a client, the operating system maybe a commercially available operating system such as Microsoft® Windows7®. An object-oriented programming system, such as the Java™ programmingsystem, may run in conjunction with the operating system and providescalls to the operating system from Java™ programs or applicationsexecuting on data processing system 200.

As a server, data processing system 200 may be, for example, an IBMeServer™ System p® computer system, Power™ processor based computersystem, or the like, running the Advanced Interactive Executive (AIX®)operating system or the LINUX® operating system. Data processing system200 may be a symmetric multiprocessor (SMP) system including a pluralityof processors in processing unit 206. Alternatively, a single processorsystem may be employed.

Instructions for the operating system, the object-oriented programmingsystem, and applications or programs are located on storage devices,such as HDD 226, and may be loaded into main memory 208 for execution byprocessing unit 206, The processes for illustrative embodiments of thepresent invention may be performed by processing unit 206 using computerusable program code, which may be located in a memory such as, forexample, main memory 208, ROM 224, or in one or more peripheral devices226 and 230, for example.

A bus system, such as bus 238 or bus 240 as shown in FIG. 2, may becomprised of one or more buses. Of course, the bus system may beimplemented using any type of communication fabric or architecture thatprovides for a transfer of data between different components or devicesattached to the fabric or architecture. A communication unit, such asmodem 222 or network adapter 212 of FIG. 2, may include one or moredevices used to transmit and receive data. A memory may be, for example,main memory 208, ROM 224, or a cache such as found in NB/MCH 202 in FIG.2.

As mentioned above, in some illustrative embodiments the mechanisms ofthe illustrative embodiments may be implemented as application specifichardware, firmware, or the like, application software stored in astorage device, such as HDD 226 and loaded into memory, such as mainmemory 208, for executed by one or more hardware processors, such asprocessing unit 206, or the like. As such, the computing device shown inFIG. 2 becomes specifically configured to implement the mechanisms ofthe illustrative embodiments and specifically configured to perform theoperations and generate the outputs described hereafter with regard tosearch engine domain transfer.

Those of ordinary skill in the art will appreciate that the hardware inFIGS. 1 and 2 may vary depending on the implementation. Other internalhardware or peripheral devices, such as flash memory, equivalentnon-volatile memory, or optical disk drives and the like, may be used inaddition to or in place of the hardware depicted in FIGS. 1 and 2. Also,the processes of the illustrative embodiments may be applied to amultiprocessor data processing system, other than the SMP systemmentioned previously, without departing from the spirit and scope of thepresent invention.

Moreover, the data processing system 200 may take the form of any of anumber of different data processing systems including client computingdevices, server computing devices, a tablet computer, laptop computer,telephone or other communication device, a personal digital assistant(PDA), or the like. In some illustrative examples, data processingsystem 200 may be a portable computing device that is configured withflash memory to provide non-volatile memory for storing operating systemfiles and/or user-generated data, for example. Essentially, dataprocessing system 200 may be any known or later developed dataprocessing system without architectural limitation.

The illustrative embodiments provide mechanisms to return a ranked listof domain-specific documents in response to a query. FIGS. 3A and 3Billustrate search engine domain transfer in accordance with anillustrative embodiment. With reference to FIG. 3A, search engine 1 310receives a query and identifies documents in domain 1 311 that satisfythe query. Search engine 1 310 is indexed for domain 1 311. In thedepicted example, domain 311 may be a massive corpus hyperlinkeddocuments (e.g., the Internet). Search engine 1 310 may use logs of userbehavior at large scale and fine resolution.

Turning to FIG. 3B, search engine 2 320 receives a query for domain 2321. Transform 322 transforms the query into multiple queries for searchengine 1 (SE 1) 323. Each instance of SE 1 323 searches a general corpusof documents, such as domain 1 311 in FIG. 3A, using a respective one ofthe multiple queries. Compare component 324 compares the results from SE1 323 to the documents in domain 2 321 to generate results.

FIG. 4 is a block diagram illustrating a search engine with domaintransfer in accordance with an illustrative embodiment. Transformcomponent 410 receives a narrow-domain query and transforms the queryinto a plurality of general-domain queries. Transform component 410consists of a plurality of transformation functions, and eachtransformation function produces a corresponding general-domain queryfrom the narrow-domain query. The transformation functions may be assimple as passing the query through unmodified or as complex asperforming translation into a general-domain language and filtering outunnecessary words via syntactic analysis. The transformation functionsdescribed herein are exemplary, and more or fewer transformationfunctions may be implemented within the spirit and scope of theillustrative embodiments.

Each of the plurality of general-domain search engine instances (S) 420receives a respective general-domain query. Each general-domain searchengine instance 420 returns results in the form of a ranked list ofdocuments, including content. For example, for each document in theranked list, the content may include a title, an excerpt, a host name,at least one uniform resource locator (URL), and the document itself.

Each general-domain search engine instance 420 may run on the samemachine. For example, general-domain search engine instances 420 mayexecute within virtual machines on the same server in parallel.Alternatively, each general-domain search engine instance 420 may run ona separate server. In one embodiment, the mechanism of the illustrativeembodiment sends the general-domain queries to an external search enginein parallel.

Featurizer 430 receives results from each general-domain search engineinstance 420 and extracts a feature vector that represents the user'squery in the general domain.

Compare component 440 compares the feature vector from featurizer 430 toeach document D_(i) in the narrow domain. Compare component 440 thengenerates a quantitative score for the narrow domain query with eachdocument based on the general-domain feature vector. There isconsiderable flexibility available for selection of a scoring function.The search engine then returns the documents to the user sorted by thequantitative score.

FIG. 5 is a block diagram illustrating query transformation for searchengine domain transfer in accordance with an illustrative embodiment. Inthe illustrative embodiment, training data are available in the form oftraining queries with ground truth labels. Each label is a document fromthe domain corpus that is most relevant to the query.

Transformation block 510 consists of two stages. First thetransformation block 510 splits the query into two versions: theoriginal query and a version consisting of only the “keywords” from thequery. In one embodiment, the transformation block 510 filters thekeywords based on parts-of-speech tags assigned to each word: nouns,verbs, adjectives, most adverbs, and wh-qualifiers/determiners/adverbs.

In the second stage, transformation block 510 contextualizes theresulting queries by the addition of context strings. For instance, thedomain relevant to veterans of the United States military might choosethe following context strings: “”, “Veteran”, “Military”, “VA”.Transformation block 510 transforms each query into a plurality ofgeneral-domain queries by concatenating the relevant context.

To illustrate, suppose the original query was given by, “Health carecoverage is okay.” The keyword-distilled version would then be, “Healthcare coverage okay.” According to the above United States militarycontext, the transformed queries would be as follows:

“Health care coverage is okay”

“Veteran Health care coverage is okay”

“Military Health care coverage is okay”

“VA Health care coverage is okay”

“Health care coverage okay”

“Veteran Health care coverage okay”

“Military Health care coverage okay”

“VA Health care coverage okay”

These are cumulatively referred to as the transformed queries or thegeneral-domain queries.

In general-domain search block 520, each transformed query isindependently passed to a general-domain search engine instance 521.Each general-domain search engine 521 returns a ranked list ofdocuments, including document title, excerpt, etc., for a correspondinggeneral-domain query. The recall of this list can generally be as largeas desired. In one example embodiment, a recall of 100 documents is morethan sufficient. General-domain search block 520 passes these results tofeaturizer block 530.

There may be many ways to featurize lists of results. In one simpleexample, each featurizer 531 concatenates titles and excerpts (alsoreferred to as “snippets”) and vectorizes viaterm-frequency-inverse-document-frequency (TF-IDF). To compare theinverse document frequencies, featurizer block 530 assumes that acollection of queries are being passed through the system in batch.

1. For each general-domain query, featurizer block 530 concatenates thetitles and snippets (with spacing) into a single long text string.

2. The featurizer block 530 then collects the concatenated text stringfor each general-domain query for a large collection of queries andcomposes a list of strings.

3. The featurizer block 530 then applies TF-IDF to each concatenatedstring described as a bag of words, computes term frequency (TF) foreach concatenated string, and computes inverse document frequency (IDF)across the collection of concatenated strings.

This process produces a vector for each transformation. Featurizer block530 generates a feature vector 532 as the concatenation of thesevectors. Featurizer block 530 then passes the feature vector 532 tocomparison block 540.

The comparison block 540 computes a similarity score based on featurevector 532 and each document D_(i) in the narrow domain. In oneembodiment, classifier 541 is a machine learning classifier trained withsupervision to produce appropriate scores. More specifically, assumingthat a set of training queries and corresponding ground truth documentlabels, for each possible document label, a one-versus-rest classifieris trained to predict whether an input feature vector has a given label.When a new query appears, classifier 541 uses the score from eachdocument's classifier to rank the documents. Supervision is notnecessary for comparison block 540; however, in accordance with theexample embodiment, a supervised machine learning classifier improvesperformance.

In the above, the feature vector 532 consists solely ofsearch-engine-domain-transfer components, and comparison block 540 usesthe entire concatenated vector to train classifier 541. In addition, thesub-vectors corresponding to different forms of the query (restricted toeach specific context or keyword distilled) provide a set of partiallycorrelated characterizations of the original query with different setsof features masked off. Each sub-vector can be used to train classifier541. The entire set of classifiers could then be used in an ensemblelearning scheme, which could reduce the risk of overfitting and yieldimproved accuracy on unseen data.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

FIG. 6 is a flowchart illustrating operation of a mechanism for searchengine domain transfer in accordance with an illustrative embodiment.Operation begins (block 600), and the mechanism receives a query (block601). The mechanism transforms the query into a plurality oftransformed, general-domain queries (block 602). The mechanism maytransform the query by passing the query unmodified, reducing the queryto only keywords, or concatenating context strings.

The mechanism performs a general domain search using the plurality oftransformed queries (block 603). The mechanism then featurizes resultsof the general domain search to form a feature vector (block 604). Themechanism may featurize results by concatenating title, excerpt, hostname, uniform resource locator (URL), and document content. Themechanism may also featurize the results by determiningterm-frequency-inverse-document-frequency (TF-IDF).

The mechanism compares each document in the specialized (narrow) domainto the feature vector to form a score (block 605). Comparison mayinclude using a machine learning classifier that is trained with a setof training queries with corresponding ground truth labels. The machinelearning classifier may predict whether an input feature vector has agiven label. Other techniques for comparison, classification, and/orscoring may be used within the spirit and scope of the illustrativeembodiments.

The mechanism then ranks documents in the specialized domain by scoreand generates a result set (block 606). The mechanism then presents theresult set to the user (block 607). Thereafter, operation ends.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

As noted above, it should be appreciated that the illustrativeembodiments may take the form of an entirely hardware embodiment, anentirely software embodiment or an embodiment containing both hardwareand software elements. In one example embodiment, the mechanisms of theillustrative embodiments are implemented in software or program code,which includes but is not limited to firmware, resident software,microcode, etc.

A data processing system suitable for storing and/or executing programcode will include at least one processor coupled directly or indirectlyto memory elements through a system bus. The memory elements can includelocal memory employed during actual execution of the program code, bulkstorage, and cache memories which provide temporary storage of at leastsome program code in order to reduce the number of times code must beretrieved from bulk storage during execution.

Input/output or I/O devices (including but not limited to keyboards,displays, pointing devices, etc.) can be coupled to the system eitherdirectly or through intervening I/O controllers. Network adapters mayalso be coupled to the system to enable the data processing system tobecome coupled to other data processing systems or remote printers orstorage devices through intervening private or public networks. Modems,cable modems and Ethernet cards are just a few of the currentlyavailable types of network adapters.

The description of the present invention has been presented for purposesof illustration and description, and is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the describedembodiments. The embodiment was chosen and described in order to bestexplain the principles of the invention, the practical application, andto enable others of ordinary skill in the art to understand theinvention for various embodiments with various modifications as aresuited to the particular use contemplated. The terminology used hereinwas chosen to best explain the principles of the embodiments, thepractical application or technical improvement over technologies foundin the marketplace, or to enable others of ordinary skill in the art tounderstand the embodiments disclosed herein.

1. A method, in a data processing system, for search engine domaintransfer, the method comprising: receiving, by the data processingsystem, an input query to search a specialized domain from a user;performing, by the data processing system, a general-domain search basedon the input query to generate a set of general-domain results;generating, by the data processing system, a feature vector based on thegeneral-domain results; generating, by the data processing system, ascore for each document within the specialized domain based on thefeature vector; generating, by the data processing system, a rankedresult set of documents based on the scores of the documents in thespecialized domain; and presenting, by the data processing system, theranked result set to the user.
 2. The method of claim 1, whereinperforming the general-domain search comprises transforming the inputquery into a plurality of general-domain queries and performing thegeneral-domain search for each of the plurality of general-domainqueries.
 3. The method of claim 2, wherein transforming the input queryinto the plurality of general-domain queries comprises concatenatingeach of a plurality of context strings to the original query version andto the keyword-only version, wherein the plurality of context stringsare relevant to the specialized domain.
 4. The method of claim 3,wherein transforming the input query further comprises splitting theinput query into an original query version and a keyword-only version.5. The method of claim 2, wherein generating the feature vectorcomprises generating a sub-vector based on general-domain search resultsfor each of the plurality of general-domain queries and concatenatingsub-vectors for the plurality of general-domain queries to form thefeature vector.
 6. The method of claim 2, wherein generating the featurevector comprises: concatenating titles and snippets from the generaldomain search results for each of the plurality of general-domainqueries to form a text string; computing term frequency (TF) for eachtext string; and computing inverse document frequency (IDF) across acollection of the text strings for the plurality of general-domainqueries; forming a plurality of TF-IDF sub-vectors based on the termfrequencies of the text strings and the inverse document frequency forthe plurality of general-domain queries; and generating the featurevector by concatenating the plurality of TF-IDF sub-vectors.
 7. Themethod of claim 1, wherein generating the feature vector comprisesconcatenating title and snippets from the general-domain results into atext string.
 8. The method of claim 1, wherein generating a score foreach document within the specialized domain comprises comparing thefeature vector to each document within the specialized domain.
 9. Themethod of claim 8, wherein comparing the feature vector to each documentwithin the specialized domain comprises applying a machine learningclassifier.
 10. The method of claim 9, wherein the machine learningclassifier is trained using a training set of queries and correspondingground truth document labels.
 11. The method of claim 9, wherein thefeature vector comprises sub-vectors corresponding to different forms ofthe query, wherein the sub-vectors provide a set of partially correlatedcharacterizations of the input query with different sets of featuresmasked off; and wherein comparing the feature vector to each documentwithin the specialized domain further comprises using each sub-vector totrain a classifier to form a set of classifiers and classifying, thedocument based on the feature vector using the set of classifiers in anensemble learning scheme. 12-20. (canceled)